Opportunity constraint economic dispatching method considering wind curtailment and load curtailment

Abstract

The invention discloses an opportunity constraint economic dispatching method considering wind curtailment and load curtailment. The opportunity constraint economic dispatching method comprises the steps of obtaining a total operation cost function of an economic dispatching model under high permeability of new energy; reducing the standby demand of economic dispatching of the system through a wind curtailment and load curtailment plan, wherein the wind curtailment and load curtailment plan comprises setting a planned output upper limit of a wind power plant and a load curtailment upper limit of a load aggregate; performing linearization processing on the opportunity constraint through a conditional risk value CVaR, a risk value VaR and a piecewise linearization method; setting the standby constraint as an opportunity constraint, enabling standby scheduling to meet a constraint requirement with a preset confidence coefficient, and allowing a standby insufficiency condition with a preset probability; an energy storage unit is integrated, the wind curtailment amount is reduced through charging and discharging operation, the standby requirement is relieved, and peak load shifting is achieved. According to the invention, through an innovative opportunity constraint economic dispatching model, the wind and load abandoning phenomenon is significantly reduced, and the risk of insufficient reserve is effectively relieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid system scheduling, in particular to an opportunity constraint economic dispatch method considering wind curtailment and load curtailment. BACKGROUND

[0002] At present, renewable energy power generation is developing rapidly, especially wind power. It plays an important role in power supply. However, wind power generation is affected by natural conditions and has the characteristics of intermittency and volatility, which may lead to the fatigue of regulation resources in power system operation, resulting in wind curtailment and load curtailment. Reasonable standby, energy storage and scheduling arrangement is an effective method to reduce these phenomena.

[0003] The opportunity constraint economic dispatch can make full use of the uncertainty distribution information of renewable energy, so that the dispatch personnel can balance the relationship between the risk and cost of dispatching decision. However, the traditional chance-constrained economic dispatch (CCED) model implicitly assumes that wind / load power is not schedulable, and the model may not be feasible in the case of wind curtailment or load curtailment.

[0004] In related technologies, the scene generation method relies on the enumeration of discrete scenes, but the optimization result may be deviated due to insufficient or inaccurate scene distribution; the robust optimization needs to ensure that all constraints are established in the worst case, resulting in conservative scheme and high cost; the opportunity constraint method relaxes the strict feasibility requirement through probability constraint, which can reduce the cost of wind curtailment and the configuration of standby capacity, and control the risk within an acceptable range.

[0005] The opportunity constraint usually involves nonlinear expression of probability distribution, resulting in non-convex model and difficulty in direct solution. Usually, analytical methods such as convex relaxation, distribution robust optimization, and simulation methods such as Monte Carlo simulation are used to transform the opportunity constraint into deterministic constraint for optimization. The simulation method is intuitive and simple, and the more the sampling times, the more accurate the result, but it brings huge computational burden. The analytical method can directly substitute the opportunity constraint into the model for optimization and solution. CVaR can transform the opportunity constraint into a convex problem, but it will bring a bilinear structure. Benders decomposition can transform the CCED bilinear problem into a master problem and a subproblem. Although the solving speed is very fast, this kind of method is not easy to find a feasible cut and requires the subproblem to be linear structure, which has certain limitations. The column and constraint generation increases the model solving scale by several times with the increase of iteration times. If it is difficult to find the optimal solution, the calculation amount is huge. The piecewise linear method is not limited to the framework of the model, and the structure is simple and the calculation efficiency is fast in small and medium-sized systems. Its continuity ensures the overall smoothness of the model, and appropriate segmentation number can ensure a certain accuracy.

[0006] Therefore, how to solve the dispatching problem of the existing power system based on the chance constraint method and the piecewise linear method is urgently needed by those skilled in the art. SUMMARY

[0007] Therefore, the application provides an opportunity constraint economic dispatching method considering abandoned wind and load, which combines with the energy storage unit, and alleviates the risk of insufficient reserve in the dispatching of the power system and reduces the situation of abandoned wind and load under the goal of ensuring the minimum operation cost.

[0008] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0009] The application provides an opportunity constraint economic dispatching method considering abandoned wind and load, comprising the following steps:

[0010] An overall operation cost function of the economic dispatching model under high penetration of new energy is obtained, wherein the overall operation cost function comprises a coal-fired cost of a thermal power unit, a reserve capacity cost, an expected reserve adjustment cost, an expected abandoned wind cost and an expected abandoned load cost;

[0011] The reserve demand of the system economic dispatching is reduced through an abandoned wind and load plan, wherein the abandoned wind and load plan comprises setting an upper limit of planned output of a wind farm and an upper limit of abandoned load of a load aggregate;

[0012] The opportunity constraint is linearly processed through a conditional risk value CVaR, a risk value VaR and a piecewise linearization method;

[0013] The reserve constraint is set as an opportunity constraint, so that the reserve dispatching meets the constraint requirement with a preset confidence, and allows a reserve insufficient situation with a preset probability;

[0014] The energy storage unit is integrated, the abandoned wind amount is reduced, the reserve demand is alleviated, and peak shaving and valley filling are realized.

[0015] In one embodiment, the overall operation cost function is as follows:

[0016]

[0017] Wherein, C is the overall operation cost of daily operation, T is a set of time, I is a set of thermal power units, J is a set of wind farms, K is a set of load aggregates, t, i, j and k are indexes corresponding to the above sets, CG i,t (p i,t ) is the coal-fired cost of the thermal power unit, p i,t is the output of the thermal power unit; is the reserve capacity cost, is the uplink and downlink reserve capacity of the i-th unit at t; is the expected reserve adjustment cost, are random variables, respectively, representing the up-regulation power and down-regulation power of the i th unit at t; is the expected wind curtailment cost, is the wind curtailment random variable; is the expected load curtailment cost, is the load curtailment random variable.

[0018] In one embodiment, the coal-fired cost function is processed by piecewise linearization, specifically:

[0019]

[0020] where a i , b i and c i are the cost coefficients of the respective terms; N ss is the set of piece numbers, s is the corresponding piece index; p i,t,s is the length of each piece of thermal power output, i.e. the actual output of the thermal unit i at t in the s th piece, and the piece number is represented as N pit . is the initial coal-fired cost, i.e. the system operating cost when the unit i operates at the minimum output state; k i,s is the slope of each piece; p i,t is the planned output of the conventional unit; p i is the minimum technical output of the thermal unit i; is the upper limit of the output of the thermal unit i in each piece s of the coal-fired cost; is the installed capacity of the thermal unit i.

[0021] The reserve capacity cost is expressed as:

[0022]

[0023] The expected reserve regulation cost is expressed as:

[0024]

[0025] where C up is the upper reserve cost coefficient; C dn is the lower reserve cost coefficient; is the up and down reserve capacity of the i th unit at t; are random variables, respectively, representing the up-regulation power and down-regulation power of the i th unit at t; N s is the set of scenario numbers, n is the index of the scenario; is the upper reserve regulation cost coefficient; is the lower reserve regulation cost coefficient; representing the decision variable of actual situation, expressed as the up / down reserve regulation capacity of thermal power unit i at scenario n, time t; N rs is the number of scenarios; E[·] is the expected value of distribution;

[0026] The expected wind curtailment cost is specifically expressed as:

[0027]

[0028] The expected load curtailment cost is specifically expressed as:

[0029]

[0030] wherein, C wc is the wind curtailment cost coefficient; C ls is the load shedding cost coefficient; is the load curtailment of load aggregate k at scenario n, time t; is the wind curtailment of wind farm j at scenario n, time t.

[0031] In an embodiment, the system economic dispatch reserve requirement is reduced by the wind curtailment and load curtailment plan, specifically:

[0032] The wind curtailment and load curtailment constraint is set, specifically expressed as:

[0033]

[0034] wherein, is the allowed upper limit of wind farm planned output; is the sum of allowed lower limits of wind farm planned output; is the wind curtailment of wind farm j at scenario n, time t, expressed as the part of wind farm actual output exceeding the allowed upper limit of wind farm; is the load curtailment of load aggregate k at scenario n, time t, expressed as the sum of parts of wind farm actual output exceeding the allowed lower limit of wind farm; is the allowed upper limit of load curtailment; w j,t is the wind farm planned output; is the installed capacity of wind turbine j; is the actual output of wind farm j at scenario n, time t.

[0035] In an embodiment, the chance constraint is linearized by the conditional risk value CVaR, risk value VaR and piecewise linearization method, including:

[0036] (a) reconstructing the chance constraint by CVaR, VaR and duality theorem;

[0037] (b) performing piecewise linearization transformation on the bilinear problem in the chance constraint after reconstruction.

[0038] In one embodiment, the chance constraint is reconstructed by using CVaR, VaR and duality theorem, including:

[0039] The function form of the chance constraint in economic dispatch is obtained, and the specific expression is:

[0040] Pr(ξ≤f(x))≥η

[0041] Wherein, ξ is a random variable; η is the confidence level;

[0042] The function form of CVaR and VaR is obtained, and the specific expression is:

[0043]

[0044]

[0045] Wherein, is the probability density function of the random variable ξ; φ(α) is the cumulative probability function of the random variable ξ taking value α; α is the value of VaR.

[0046] The discrete form of CVaR is obtained, and the specific expression is:

[0047]

[0048] The transformed form of the chance constraint is obtained through the discrete form of CVaR and the equivalent form of VaR, and the specific expression is:

[0049]

[0050] θ n ≥ξ n -α,θ n ≥0,n=1,2,…,N rs

[0051] Wherein, ξ n -α is replaced by θ n ;

[0052] The transformed form of the above constraint is obtained by duality theorem and quantile substitution, and the specific expression is:

[0053] f(x)≥Q

[0054]

[0055] θ n ≥ξ n -α,θ n ≥0,n=1,2,…,N rs

[0056]

[0057] where Q is the η quantile of the random variable ξ; λ n is the dual variable.

[0058] In one embodiment, piecewise linearization transformation is implemented for the bilinear problem in the reformulated chance constraint, including:

[0059] The function form of the bilinear problem is obtained, and the specific expression is:

[0060] p = xy

[0061] The expression function after linearization is obtained, and the specific expression is:

[0062]

[0063] xy i ·z i = xy i -t i

[0064]

[0065] where y i is the i-th piecewise variable of the continuous variable y; t i is the equivalent alternative continuous variable; z i is the binary variable of the piece i; l and u are constants that limit the continuous variable x.

[0066] In one embodiment, the reserve constraint is set as a chance constraint, and the expression is as follows:

[0067]

[0068] where w j,t is the planned output of the wind farm; is the random output of the wind farm; 1-β is the confidence level;

[0069] The remaining reserve-related constraints are obtained, and the specific expression is:

[0070]

[0071] where: is the up ramp rate; is the down ramp rate; ΔT is the time interval, in h; is the upper and lower limit of the thermal power unit output; π i is the participation coefficient of each thermal power unit.

[0072] In one embodiment, the integrated energy storage unit reduces the amount of curtailed wind power, alleviates the reserve demand, and realizes peak load shifting through charging and discharging operations; comprising:

[0073] The energy storage unit is related to wind power and thermal power through power balance constraints, and the specific expression is:

[0074]

[0075] Wherein, d k,t is the system load; is the energy storage charging power; is the energy storage discharging power; p i,t is the planned output of the conventional unit; w j,t is the planned output of the wind farm;

[0076] The specific expression is limited by the energy storage charging and discharging constraints, and the specific expression is:

[0077]

[0078] Wherein, N e is the number of energy storage units; is a binary variable of the state of the energy storage unit; SoC e,t is the state of charge of the energy storage unit; is the initial charge; is the upper and lower limit of the state of charge of the energy storage unit; E e is the energy storage capacity of each energy storage unit; is the energy storage charging and discharging power; is the upper and lower limit of the energy storage charging and discharging power; E is the set of energy storage units; e is the index of the energy storage unit.

[0079] As can be seen from the above technical solution, compared with the prior art, the technical advantage of the present application is that through the innovative opportunity constraint economic dispatching model, a balance between cost and risk is found, the phenomenon of curtailed wind power and curtailed load is significantly reduced, and the risk of insufficient reserve is effectively alleviated. Specifically, the CVaR / VaR method is used to convert the probability constraint into a solvable deterministic form, the bilinear problem is handled by piecewise linearization, the calculation complexity is reduced, and the dispatching decision is more efficient. In addition, the integrated energy storage unit realizes peak load shifting, reduces the amount of curtailed wind power, and assists in reserve regulation, thereby improving the stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creating any inventive labor.

[0081] Figure 1 Flow chart of the opportunity constraint economic dispatch method considering wind power and load curtailment provided by the present application;

[0082] Figure 2 Schematic diagram of the role of the opportunity constraint in the present application;

[0083] Figure 3 Time sequence diagram of the model implementation process in the present application;

[0084] Figure 4 Structure diagram of the 39-node system in the present application;

[0085] Figure 5 Proportion diagram of the load and controllable load in the present application;

[0086] Figure 6 Power balance accumulation diagram in the present application;

[0087] Figure 7 Cost proportion sector diagram in the present application;

[0088] Figure 8 Schematic diagram of the impact of the reserve regulation capacity and the reserve capacity on the planned wind power output in the present application;

[0089] Figure 9 Schematic diagram of the impact of the energy storage power change on the wind power curtailment in the present application;

[0090] Figure 10 Schematic diagram of the planned wind power output at different confidence levels in the present application. DETAILED DESCRIPTION

[0091] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0092] The embodiments of the present application disclose an opportunity constraint economic dispatch method considering wind power and load curtailment. The present application considers the uncertainty of wind power output, generates random wind farm output data through a Gaussian mixture model, then establishes an opportunity constraint economic dispatch model of a power system, the model considers reserve shortage to formulate a dispatch scheme of wind power and load curtailment, and reduces the wind power curtailment through energy storage, so that the system is economically operated. Secondly, an opportunity constraint reconstruction method based on conditional value at risk, value at risk and dual theory is proposed, and a piecewise linear method is used to solve the bilinear problem, so that the original problem is converted into a mixed integer linear programming problem for solving. Finally, the feasibility and effectiveness of the method are verified through example analysis.

[0093] The application provides an opportunity constraint economic dispatch method considering abandoned wind and load, solves the problems of the prior art, and refers to Figure 1 As shown in the figure, the method specifically comprises the following steps S1-S5:

[0094] S1. Obtain a total operation cost function of an economic dispatch model under high penetration of new energy, wherein the total operation cost function comprises a coal-fired cost of a thermal power unit, a reserve capacity cost, an expected reserve adjustment cost, an expected abandoned wind cost and an expected abandoned load cost;

[0095] S2. Reduce the reserve demand of system economic dispatch through an abandoned wind and load plan, wherein the abandoned wind and load plan comprises setting an upper limit of planned output of a wind farm and an upper limit of abandoned load of a load aggregate;

[0096] S3. Linearize the opportunity constraint through a conditional risk value CVaR, a risk value VaR and a piecewise linearization method;

[0097] S4. Set the reserve constraint as an opportunity constraint, so that the reserve dispatch meets the constraint requirement with a preset confidence, and allows a reserve deficiency with a preset probability;

[0098] S5. Integrate an energy storage unit, reduce the abandoned wind amount, relieve the reserve demand and realize peak shaving and valley filling through charging and discharging operations.

[0099] The total operation cost function of step S1 is regarded as a model of a dispatch problem of a power system, and the operation cost thereof is mainly concerned. Unit combination arrangement focuses on long-term economy, and is usually calculated and processed for unit start-stop arrangement in a week in a week in advance, although the safety of economic dispatch is often ignored. For the purpose of analysis simplicity, a unit full-on scene is selected for analysis, and the start-stop cost of the unit is not considered. The day-ahead economic dispatch cost comprises the coal-fired cost of the thermal power unit and the reserve capacity cost. The reserve in the real-time stage also produces a corresponding cost (power cost and loss compensation of unit adjustment). In addition, from the actual point of view, the abandoned wind and load plan should be arranged in advance by dispatch personnel, and when the abandoned wind and load occurs in the real-time stage, a corresponding penalty cost is produced.

[0100] Specifically, the total operation cost function comprises:

[0101] (1) the coal-fired cost of the thermal power unit; (2) the reserve capacity cost; (3) the expected reserve adjustment cost; (4) the cost of the expected abandoned wind and load plan.

[0102] Obtain a target function of the day operation cost of each link, and the specific expression is as follows:

[0103]

[0104] where C is the total operation cost, T is the set of time, I is the set of thermal power units, J is the set of wind farms, K is the set of load aggregates, t, i, j and k are the indices of the above sets, CG i,t i,t is the coal cost of the thermal power unit, p i,t is the output of the thermal power unit; is the reserve capacity cost, is the up and down reserve capacity of the i-th unit at time t; is the expected reserve regulation cost, is a random variable, which represents the up and down regulation power of the i-th unit at time t, respectively; is the expected wind curtailment cost, is the wind curtailment random variable; is the expected load curtailment cost, is the load curtailment random variable.

[0105] where the coal cost is expressed as:

[0106]

[0107]

[0108] where a i , b i and c i are the cost coefficients of each term, respectively. N ss is the set of segment numbers, s is the corresponding segment index, p i,t,s is the length of each segment of the thermal power output, i.e. the actual output of the i-th thermal power unit at the s-th segment of time t, and the segment number is represented as N pit . is the initial coal cost, i.e. the system operation cost when the unit i operates at the minimum output state; k i,s is the slope of each segment; p i,t is the planned output of the conventional unit; p i is the minimum technical output of the thermal power unit i; is the upper limit of the output of each segment s of the coal cost of the thermal power unit i; is the installed capacity of the thermal power unit i.

[0109] where the reserve-related cost is expressed as:

[0110]

[0111] where C up is the up reserve cost coefficient; C dn is the down reserve cost coefficient; ​is the up / down reserve capacity of the ith unit at time t; is a random variable, representing the up / down regulation power of the ith unit at time t, respectively; s is the set of scenario numbers, n is the index of scenario; is the up reserve regulation cost coefficient; is the down reserve regulation cost coefficient; represents is the decision variable of the actual situation, expressed as the up / down reserve regulation capacity of the thermal power unit i at time t in scenario n; rs is the scenario number; E[·] is the expected value of the distribution.

[0112] where the wind curtailment and load shedding cost is represented as:

[0113]

[0114]

[0115] where C wc is the wind curtailment cost coefficient; C ls is the load shedding cost coefficient; is the load shedding of the load aggregation k at time t in scenario n; is the wind curtailment of the wind farm j at time t in scenario n.

[0116] In steps S2-S5: form the economic dispatch related basic constraints considering wind curtailment and load shedding.

[0117] Specifically:

[0118] Get the power balance constraint, which is specifically represented as:

[0119]

[0120] where d k,t is the system load; is the energy storage charging power; is the energy storage discharging power; p i,t is the planned output of the conventional unit; w j,t is the planned output of the wind farm;

[0121] Get the related constraints of thermal power units and wind power units, which are specifically represented as:

[0122]

[0123] where, is the up ramp rate; is the down ramp rate; ΔT is the time interval, in h; is the upper and lower limit of the thermal power unit output; Pj is the upper limit of the wind turbine output.

[0124] The wind curtailment and load curtailment plan constraints are obtained, which are specifically shown as follows:

[0125]

[0126]

[0127] wherein, Pj is the upper limit of the wind farm planned output; Pj is the sum of the lower limit of the wind farm planned output; Pj is the wind curtailment of the wind farm j in scenario n at time t, which is expressed as the part of the actual output of the wind farm exceeding the upper limit of the wind farm; Pj is the sum of the load curtailment of the load aggregation k in scenario n at time t, which is expressed as the part of the actual output of the wind farm exceeding the lower limit of the wind farm; Pj is the upper limit of the load curtailment; w j,t Pj is the wind farm planned output; Pj is the installed capacity of the wind turbine j; Pj is the actual output of the wind farm j in scenario n at time t.

[0128] The constraint containing the max function can be linearized by using the big M method. For the problem similar to x = max{y, 0}, the linearization process is as follows, which is specifically shown as follows:

[0129] x ≤ y + (1-δ)M

[0130] x ≥ y - (1-δ)M

[0131] x ≤ δM

[0132] y ≤ δM

[0133] x ≥ 0

[0134] wherein, δ is a 0-1 variable; M is a very large constant; x and y are variables.

[0135] The reserve related constraints are obtained, which are specifically shown as follows:

[0136]

[0137] wherein: is the up ramp rate; is the down ramp rate; ΔT is the time interval, in h; Pj is the upper and lower limits of the thermal power unit output; π i Pj is the participation coefficient of each thermal power unit; Pj is the actual wind power output of scenario n at time t.

[0138] The energy storage charging and discharging related constraints are obtained, and specifically represented as:

[0139]

[0140] wherein, N e is the number of energy storage units; is the binary variable of the state of charge and discharge of the energy storage unit; SoC e,t is the state of charge of the energy storage unit; is the initial electric quantity; is the upper and lower limit of the state of charge of the energy storage unit; E e is the energy storage capacity of each energy storage unit; is the energy storage charging and discharging power; is the upper and lower limit of the energy storage charging and discharging power; E is the set of energy storage units; and e is the index of the energy storage unit.

[0141] The power flow safety related constraints are obtained, and specifically represented as:

[0142]

[0143] wherein, L is the set of transmission lines, and l is the index of the corresponding transmission line; F l,j , F l,k and F l,i are the transfer distribution factors, respectively corresponding to the wind farm node, the load aggregation node and the thermal power unit node.

[0144] As shown in FIG. 3, it is a schematic diagram of the action principle of the chance constraint, which shows the action mechanism of the chance constraint. Figure 2

[0145] The chance constraint (related to reserve) is obtained, and specifically represented as:

[0146]

[0147] wherein, is the random output of the wind farm; and 1-β is the confidence level; is the random load shedding of the controllable load; is the random wind shedding of the wind farm.

[0148] Specifically, the step S3 is solved by the conditional risk value CVaR, the risk value VaR and the chance constraint reconstruction method of the dual theory, and by the piecewise linear method to solve the bilinear problem, so as to convert the original problem into a mixed integer linear programming problem for solving.

[0149] Specifically, the step S3 is solved by the conditional risk value CVaR, the risk value VaR and the chance constraint reconstruction method of the dual theory, and by the piecewise linear method to solve the bilinear problem, so as to convert the original problem into a mixed integer linear programming problem for solving.

[0150] The function form of the chance constraint in economic dispatch is obtained, and the specific expression is:

[0151] ​

[0152] where X is a decision vector composed of decision variables w j,t 、 and ; and are constant matrices; is expressed as a random vector m is the opportunity constraint index of the scheduling period t (t ∈ T); the index set is denoted as M con and |M con | = 2.

[0153] The function form of CVaR and VaR is obtained, and the specific expression is as follows:

[0154]

[0155] where is the probability density function of the random variable ξ; φ(α) is the value of the cumulative probability function of the random variable ξ at α; and α is the value of VaR.

[0156] The discrete form of CVaR is obtained, and the specific expression is as follows:

[0157]

[0158] The transformed form of the opportunity constraint is obtained through the discrete form of CVaR and the equivalent form of VaR, and the specific expression is as follows:

[0159]

[0160] θ n ≥ ξ n - α, θ n ≥ 0, n = 1, 2, …, N rs

[0161] where ξ n - α is replaced by θ n .

[0162] The transformed form of the above constraint is obtained through the duality theorem and the quantile substitution, and the specific expression is as follows:

[0163] f(x) ≥ Q

[0164] Q ≥ α

[0165]

[0166] θ n ≥ ξ n - α, θ n≥0, n=1,2,…,N rs

[0167]

[0168] Where Q is the η quantile of the random variable ξ; λ n These are dual variables.

[0169] In addition, the bilinear terms in the reconstructed chance constraints are linearized, including the following steps:

[0170] The functional form of the bilinear problem is obtained as follows:

[0171] p = xy

[0172] The linearized expression function is obtained, specifically:

[0173]

[0174] xy i ·z i =xy i -t i

[0175]

[0176] Among them, y i Let t be the i-th piecewise variable of the continuous variable y; i For continuous variables that are equivalent substitutes; z i Let be a binary variable representing segment i; l and u are constants that restrict the continuous variable x.

[0177] The above method can transform chance constraints into mixed integer constraints, which can then be solved directly using the Gour BI solver. When scheduling, the dispatcher can refer to... Figure 3 The timing diagram is used for operation.

[0178] Experimental example:

[0179] This invention analyzes the IEEE 39-node system, such as... Figure 4 As shown, the system includes 46 transmission lines, 10 thermal power units, 3 wind farms, 3 energy storage units, and 5 load aggregation units. The load and controllable load account for a significant portion of the total load. Figure 5 As shown, the controllable load ratio is required to be no more than one-third of the system load. The controllable load ratios to the total load are set at 5.15%, 7.99%, 10.87%, 4.93%, and 4.53%, respectively. The installed capacity of the wind farms is set at 350, 300, and 450 MW, with the wind power penetration rate controlled at around 30%. The number of scenarios is set at 30. Table 1 shows the cost coefficients for each component.

[0180] Table 1 Cost coefficients for each component

[0181] Cost factor Unit ($ / MW) Up / down reserve 200 / 50 Up / down regulation reserve 50 / 50 Wind curtailment 600 Load curtailment 1000

[0182] like Figure 6 As shown, when the load is low, the wind farm's output accounts for a high proportion, close to 40%, while when the load is high, the wind farm's output accounts for a low proportion, about 20%, with a total wind power penetration rate of approximately 30%. Through the role of reserve regulation capacity, the total planned output of the wind farm is relatively even across different time periods, without any particularly extreme situations. When the load is low, some wind power is stored in energy storage units, while when the load is high, the energy storage units release electricity, playing a role in peak shaving and valley filling to some extent. Thermal power units remain the main source of power generation.

[0183] like Figure 7 As shown, coal-fired power unit costs account for the largest proportion, followed by standby-related costs. Standby costs and standby regulation costs account for a relatively large proportion compared to wind curtailment and load curtailment costs. This is because wind curtailment and load curtailment have large cost coefficients. The system reduces costs by increasing standby to reduce wind curtailment and load curtailment, which also stabilizes the system and ensures that wind power output is at the planned wind power output level.

[0184] like Figure 8 As shown, reserve regulation capacity plays a positive role in stabilizing the planned output of wind farms. The majority of reserve regulation resources are within the reserve capacity range; only a small portion is outside this range, which is normal given the certain confidence level, allowing for a small number of deviations. Furthermore, this reserve differs from typical load reserve; this reserve capacity is linked to both load and planned wind power output. Due to random sampling, some extreme cases result in a large range for actual wind farm output, but in reality, most wind farm output falls within the reserve regulation range. To reduce reserve regulation costs, the planned wind farm output is located around areas where actual wind farm output is concentrated. Besides reserve regulation, energy storage also plays a role in maintaining stable wind farm output.

[0185] like Figure 9 As shown, the amount of wind curtailment decreases with the increase of energy storage, proving that increasing energy storage has a positive effect on reducing wind curtailment and can also reduce the objective function cost, enabling the system to operate economically. Energy storage can also reduce the use of reserves during standby scheduling when there is no need for wind or load curtailment, provided that its capacity is sufficient. It can also reduce operating costs.

[0186] Table 2. Impact of Confidence Level on Costs of Each Component

[0187]

[0188]

[0189] As shown in Table 2, when the confidence level decreases, the value of the objective function (i.e., total cost) increases. This is because the tightening of constraints leads to a decrease in available reserve capacity, resulting in increased wind curtailment and its associated costs. Furthermore, the rate of increase in wind curtailment costs exceeds the rate of decrease in reserve costs and load curtailment costs, leading to an increase in total cost. In addition, the increased wind curtailment and reduced reserve capacity result in a decrease in planned wind power output, requiring thermal power units to generate more electricity, thus increasing coal costs. This is another significant reason for the increase in total cost; in this case, the cost of thermal power generation increases from $58.22 / MW to $59.42 / MW.

[0190] like Figure 10 As shown, when the confidence level is high, i.e., when the opportunity constraint is relaxed, the wind farm's planned output ceiling is large due to the large reserve regulation capacity and the low wind curtailment under the same conditions. Conversely, when the confidence level decreases, i.e., when the opportunity constraint tightens, the reserve regulation capacity decreases. Since the cost coefficient for load curtailment is greater than that for wind curtailment, reserve regulation resources are prioritized for upstream reserves to reduce load curtailment. Furthermore, because both upstream and downstream reserve regulation decrease, the wind farm's planned output declines, and wind curtailment increases. This situation significantly increases the cost of wind curtailment, thus increasing the total cost.

[0191] Table 3 shows the results of the wind curtailment / load curtailment planning and opportunity constraint method model.

[0192]

[0193] Table 4 Results of the model without wind / load curtailment plans and with opportunity constraint methods

[0194]

[0195]

[0196] Table 5 Results of models with and without opportunity constraints, including those with wind / load curtailment plans.

[0197]

[0198] Table 6 shows the results of the model without wind / load curtailment planning and chance constraint methods.

[0199]

[0200] As shown in Tables 3-6, models without wind / load curtailment plans consume more reserve capacity and reserve regulation capacity, and require higher reserve costs, compared to models with such plans. Therefore, models with wind / load curtailment plans play a positive role in alleviating reserve shortages and can make the system operate more economically. Models without opportunity constraints consume more reserve capacity and reserve regulation capacity, and require higher reserve costs, compared to models with opportunity constraints. The original model does not require the advance deployment of large amounts of reserve capacity and does not waste flexible resources; therefore, models with opportunity constraints have a more prominent advantage and can make the system operate more economically.

[0201] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of an opportunity-constrained economic scheduling method that takes into account wind curtailment and load abandonment.

[0202] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0203] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements an opportunity-constrained economic dispatch method that takes into account wind curtailment and load abandonment.

[0204] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0205] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0206] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An opportunity-constrained economic dispatch method considering wind curtailment and load abandonment, characterized in that, Includes the following steps: Obtain the total operating cost function of the economic dispatch model under high penetration of new energy sources. The total operating cost function includes the coal cost of thermal power units, the reserve capacity cost, the expected reserve adjustment cost, the expected wind curtailment cost, and the expected load curtailment cost. The system reduces the reserve demand for economic dispatch by implementing a wind curtailment and load curtailment plan, which includes setting a planned output limit for wind farms and a load curtailment limit for load clusters. Opportunity constraints are linearized using conditional risk value CVaR, risk value VaR, and piecewise linearization. Set the standby constraint as an opportunity constraint so that the standby scheduling meets the constraint requirements with a preset confidence level, allowing for standby shortages with a preset probability. Integrated energy storage units reduce wind curtailment and alleviate backup demand through charging and discharging operations, and achieve peak shaving and valley filling.

2. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 1, characterized in that, The total operating cost function is as follows: Where C represents the total daily operating cost; T represents the set of times; I represents the set of thermal power units; J represents the set of wind farms; K represents the set of load clusters; t, i, j, and k are the indices corresponding to the above sets; CG i,t (p i,t p represents the coal cost of a thermal power unit. i,t To provide power to thermal power units; For standby capacity costs, Let be the uplink and downlink reserve capacity of the i-th unit at time t; To account for expected reserve adjustment costs, Let be random variables, representing the upward and downward adjustment power of the i-th unit at time t, respectively; To account for the expected cost of wind curtailment, For wind curtailment, random variables are used. The expected cost of abandoning load, For discarded load random variables.

3. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 2, characterized in that, The coal cost function is processed through piecewise linearization, specifically as follows: Among them, a i b i and c i These are the cost coefficients for each item; N ss Let p be the set of segments, where s is the corresponding segment index; i,t,s The length of each segment of the thermal power output is denoted as N, which represents the actual output of thermal power unit i in the s-th segment during time period t. pit ; The initial coal cost is the system operating cost when unit i is operating at minimum output; k i,s p represents the slope of each segment. i,t Contribute to the planned operation of conventional generating units; p i Minimum technical output for thermal power unit i; The upper limit of output for each segment s of the coal-fired cost of thermal power unit i; Let i be the installed capacity of thermal power unit i; The cost of the spare capacity is expressed as follows: The expected reserve adjustment cost is expressed as follows: Among them, C up C is the reserve cost coefficient. dn The reserve cost coefficient is used for the next step. Let be the uplink and downlink reserve capacity of the i-th unit at time t; Let N be random variables, representing the upward and downward adjustment power of the i-th unit at time t, respectively; s Let n be the set of scenes, where n is the index of the scene. The reserve adjustment cost coefficient; The reserve adjustment cost coefficient; express The actual decision variables are expressed as the uplink / downlink reserve regulation capacity of thermal power unit i at time t, in scenario n; N rs denoted as the number of scenes; E[·] represents the expected value of the distribution; The expected cost of wind curtailment is specifically expressed as follows: The specific expression for the expected load abandonment cost is as follows: Among them, C wc C represents the cost coefficient for wind curtailment. ls This is the cost coefficient for load reduction; For scenario n, time t, the abandoned load of load aggregate k; Let n be the wind curtailment rate of wind farm j at time t.

4. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 3, characterized in that, The reduction of system economic dispatch reserve requirements through wind curtailment and load abandonment planning specifically includes: The constraint for wind curtailment and load abandonment is set, and the specific expression is as follows: in, The maximum allowable power output for wind farm projects; W t floor The sum of the minimum allowable limits for contributions to wind farm projects; For scenario n and time t, the wind curtailment of wind farm j is expressed as the portion of the wind farm's actual output exceeding the wind farm's allowable upper limit. Let the abandoned load of load aggregate k under scenario n and time t be expressed as the sum of the portion of the wind farm's actual output that exceeds the wind farm's allowable lower limit; This represents the maximum allowable limit for load shedding; w j,t Contribute to the wind farm project; Let j be the installed capacity of the wind turbine unit; Let n be the actual power output of wind farm j at time t.

5. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 4, characterized in that, Opportunity constraints are linearized using conditional risk value CVaR, risk value VaR, and piecewise linearization methods, including: (a) Reconstructing opportunity constraints using CVaR, VaR, and duality theorem; (b) Implement piecewise linearization transformation on the bilinear problem in the reconstructed opportunity constraint.

6. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 5, characterized in that, Reconstructing opportunity constraints using CVaR, VaR, and duality theorem, including: The functional form of the opportunity constraint in economic scheduling is obtained, specifically expressed as follows: Pr(ξ≤f(x))≥η Where ξ is a random variable; η is the confidence level; The function forms for obtaining CVaR and VaR are as follows: in, Let ξ be the probability density function of the random variable ξ; φ(α) be the cumulative probability function value α of the random variable ξ; and α be the value of VaR. The discrete form of CVaR is obtained, and the specific expression is as follows: The chance constraint transformation can be obtained from the discrete form of CVaR and the equivalent form of VaR, and the specific expression is as follows: i n ≥ξ n -a,i n ≥0,n=1,2,…,N rs Where, ξ n -α using θ n replace; The transformation formula for the above constraints is obtained through the duality theorem and quantile substitution, and its specific expression is as follows: f(x)≥Q Q≥α i n ≥ξ n -a,i n ≥0,n=1,2,…,N rs Where Q is the η quantile of the random variable ξ; λ n These are dual variables.

7. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 6, characterized in that, The piecewise linearization transformation of the bilinear problem in the reconstructed chance constraint includes: The functional form of the bilinear problem is obtained as follows: p = xy The linearized expression function is obtained as follows: xy i ·z i =xy i -t i Among them, y i Let t be the i-th piecewise variable of the continuous variable y; i For continuous variables that are equivalent substitutes; z i Let be a binary variable representing segment i; l and u are constants that restrict the continuous variable x.

8. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 4, characterized in that, Set the standby constraint as an opportunity constraint, as shown in the following expression: Among them, w j,t Contribute to the wind farm project; The wind field provides random power output; 1-β represents the confidence level. To obtain the remaining spare related constraints, the specific expression is: in: This refers to the uphill climbing rate; ΔT represents the downhill / uphill speed; ΔT represents the time interval in hours. For the upper and lower limits of the output of thermal power units; π i This represents the participation coefficient for each thermal power unit.

9. The opportunity-constrained economic dispatch method considering wind curtailment and load abandonment according to claim 8, characterized in that, Integrated energy storage units reduce wind curtailment, alleviate backup demand, and achieve peak shaving and valley filling through charging and discharging operations; including: By using power balance constraints, the energy storage unit is associated with wind power and thermal power generation, as specifically expressed in the following expression: Where, d k,t This represents the system load. Power for energy storage charging; p is the energy storage discharge power; i,t To contribute to the planned output of conventional generating units; w j,t Contribute to the wind farm project; The limitation is imposed by energy storage charge and discharge constraints, and the specific expression is as follows: Where, N e This represents the number of energy storage units; A binary variable representing the charging and discharging state of an energy storage unit; SoC e,t The energy storage unit's charge capacity; This is the initial charge level; E represents the upper and lower limits of the energy storage charge capacity. e This represents the energy storage capacity of each energy storage unit; For energy storage charging and discharging power; E represents the upper and lower limits of the energy storage charging and discharging power; E represents the set of energy storage units; and e represents the energy storage unit index.

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